Associative Classification Method Research Articles

Multi-Label Classification Based on Associations

Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. However, the current state of knowledge and the views of various specialists indicate that the issue of multi-label classification (MLC) cannot be solved by any AC method. Since this is the case, adapting or using an AC algorithm to manage multi-label datasets is one of the most pressing issues. To solve the MLC issue, this research proposes modifying the classification based on associations (msCBA) method by extending its capabilities to consider more than one class label in the consequent of its rules and modifying its rules order procedure to fit the nature of the multi-label dataset. The proposed algorithm outperforms several other MLC algorithms from various learning techniques across a variety of performance measuresand using six datasets with different domains. The main findings of this research are the significance of utilizing the local dependencies among labels compared to global dependencies, and the important rule of AC in solving the problem of MLC.

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

One of the leading cancers for both genders worldwide is lung cancer. The occurrence of lung cancer has fully augmented since the early 19th century. In this manuscript, we have discussed various data mining techniques that have been employed for cancer diagnosis. Exposure to air pollution has been related to various adverse health effects. This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer. We have introduced data mining in lung cancer to air pollution, and our approach includes preprocessing, data mining, testing and evaluation, and knowledge discovery. Initially, we will eradicate the noise and irrelevant data, and following that, we will join the multiple informed sources into a common source. From that source, we will designate the information relevant to our investigation to be regained from that assortment. Following that, we will convert the designated data into a suitable mining process. The patterns are abstracted by utilizing a relational suggestion rule mining process. These patterns have revealed information, and this information is categorized with the help of an Auto Associative Neural Network classification method (AANN). The proposed method is compared with the existing method in various factors. In conclusion, the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

Objective: This study aims to classify COVID-19 by applying the associative classification method on the gene data set consisting of open access COVID-19 negative and positive patients and revealing the disease relationship with these genes by identifying the genes that cause COVID-19.
 Method: In the study, an associative classification model was applied to the gene data set of patients with and without open access COVID-19. In this open-access data set used, 15979 genes are belonging to 234 individuals. Out of 234 people, 141 (60.3%) were COVID-19 negative and 93 (39.7%) were COVID-19 positives. In this study, LASSO, one of the feature selection methods, was performed to choose the relevant predictors. The models' performance was evaluated with accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. 
 Results: According to the study findings, the performance metrics from the associative classification model were accuracy of 92.70%, balanced accuracy of 91.80%, the sensitivity of 87.10%, the specificity of 96.50%, the positive predictive value of 94.20%, the negative predictive value of 91.90%, and F1-score of 90.50%.
 Conclusion: The proposed associative classification model achieved very high performances in classifying COVID-19. The extracted association rules related to the genes can help diagnose and treat the disease.

Modeling of the English Translation Complex System Based on Discrete Data Mining

This paper provides an in-depth analysis and research on the application of web discrete data mining in modeling complex systems for English translation. This paper adopts association rule mining, clustering, and classification methods to deeply analyze English translation, mine the interesting rules in it, find out the interinfluence relationship of system modeling and the relationship between translation behaviors, analyze and compare different data mining methods, and make an evaluation of the mining results to provide a scientific reference basis for the relevant decision-making scheme of the English translation. The paper elaborates the relevant concepts of data mining and the theoretical basis and analysis process of association rule mining, clustering rule mining, and classification rule mining methods. The application of data mining techniques in modeling and analysis of complex systems of the English translation is highlighted, and these three mining methods are used to analyze a set of English sample data: through the analysis of the correlation of different English translations, some feasible suggestions are provided for the order of translation settings; through the analysis of clustering of English translations, the shortcomings of traditional translation methods are made up; through the analysis of the factors affecting the accuracy of English translations factors, a useful reference is provided on how to improve English translation and then improve the quality of translation.

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

A design failure pre-alarming system using score- and vote-based associative classification

Design failures often incur substantial cost overruns in the shipbuilding industry. Precautions against possible design failures facilitate on-time delivery and improved productivity. However, few studies have investigated the use of accumulated knowledge to prevent ship design failure. In addition, existing associative classification (AC) methods pay little attention to the rule consolidation process whereby discriminative association rules can be aggregated. In this study, we propose a new AC method that considers both support and confidence, while the number of matching features is taken into account not only to identify specific rules that capture useful associations, but also to enhance predictive performance by effectively aggregating relevant rules. We present an empirical case for the Korean shipbuilding industry by applying the proposed method to help reduce design failures by providing a designer with the most relevant revision history for a given design task so that unnecessary rectification can be avoided. The comparative results showed that the proposed method returns the best prediction accuracy among competing AC processes.

CARs-Lands: An associative classifier for large-scale datasets

Associative classifiers are one of the most efficient classifiers for large datasets. However, they are unsuitable to be directly used in large-scale data problems. Associative classifiers discover frequent/rare rules or both in order to produce an efficient classifier. Discovery rules need to explore a large solution space in a well-organized manner; hence, learning of the associative classification methods of large datasets is not suitable on large-scale datasets because of memory and time-complexity constraints. The proposed method, CARs-Lands, presents an efficient distributed associative classifier. In CARs-Lands, first, a modified dataset is generated. This new dataset has sub-datasets that are completely appropriate to produce classification association rules (CARs) in a parallel manner. The produced dataset by CARs-Lands contains two types of instances: main instances and neighbor instances. Main instances can be either real instances of training dataset or meta-instances, which are not in the training dataset; each main instance has several neighbor instances from the training dataset, which together form a sub-dataset. These sub-datasets are used for parallel local association rule mining. In CARs-Lands, local association rules lead to more accurate prediction, because each test instance is classified by the association rules of their nearest neighbors in the training datasets. The proposed approach is evaluated in terms of accuracy on six real-world large-scale datasets against five recent and well-known methods. Experiment results show that the proposed classification method has high prediction accuracy and is highly competitive when compared to other classification methods.

An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

Associative classification (AC) is an interesting approach in the domain of data mining which makes use of the association rules for building a classification system, which are easy for interpretation by the end user. The previous work [1] showed excellent performance in a static large data base but there existed a question of same performance when applied in an incremental data. Many of the Associative Classification methods have left the problem of data insertion and optimization unattended that results in serious performance degradation. To resolve this issue, we used new technique C-NTDI for building a classifier when there is an insertion of data that take place in a non-trivial fashion in the initial data that are used for updating the classification rules and thereafter to apply the PPCE technique for the generating of rules and further Proportion of Frequency occurrence count with BAT Algorithm (PFOCBA) is applied for optimizing the rules that are generated. The experiments were conducted on 6 different incremental data sets and we found that the proposed technique outperforms other methods such as ACIM, E-ACIM, Fast Update (FUP), Galois Lattice theory (GLT) and New Fast Update (NFUP) in terms of accuracy and time complexity.

Improved Privacy Protecting in Distributed Grid Data Resource using Multiplicative Perturbation Based on Frequent Decision Classifier

The enormous amount of data process in distributed grid resource holds sensitive information on centralized access. The common privacy standard needs advancement to protect the sensitive data in various sectors like sharing, legal privacy and policies to access the data. The privacy standards depend the Distributed Data Mining (DDM) approaches like Association Rule Mining algorithms (ARM), clustering, and classification methods to preserve the data from unauthorized access. But the security standards have lacked privacy-preserving rules due to high dimensionality problems of data access leads more time complexity. To overcome the problem, to propose a Multiplicative Perturbation Swapping method based on Frequent Decision Classifier (MPS-FDC). This method is adaptive to data publishing secrecy to hold the privacy standards better than association rule prediction. This optimization resolves the forecasting leakages based on Persuasive Privacy Preserving Data Mining (P2PDM) to secure the data. Which this technique initially does the sanitization to reduce the dimensionality to remove un-variant the outliers. The data perturbation keeps the original data to modify using supportive noise delimiters with state matrix distortion (SMD). So the original data keep safe without effect from the outliers. The frequent rule prediction decides to classify the recurrentdata from unauthorized access to disclosing crypto- privacy policy. The proposed system improves the privacy standard compared to the ARM specification rules.

Comparative Study of different Lazy Learning Associative Classification Methods

Abstract Lazy Learning Associative Classification (LLAC) is a promising approach in the field of data mining. It is one of the associative classification methods in which it delays the processing of training datasets until it receives the test instance for the class prediction. Lazy learning associative classification can be constructed in two phases. Subset generation is the first phase and the subset evaluation is the second phase. In the past decades, many lazy learning associative classification methods have been proposed. These algorithms utilize several different methods for subset generation and subset evaluation. This paper focuses on comparative study of different lazy learning associative classification methods.

New Associative Classification Method Based on Rule Pruning for Classification of Datasets

In data mining, a rule-based classification approach called Associative Classification (AC) normally builds accurate classifiers from supervised learning data sets. It extracts “If-Then” rules and associates each of the generated rules with two computed parameters; support and confidence. These two parameters are utilized to differentiate the rules’ superiority during the building of a classifier’s step. In current AC algorithms, whenever a rule is inserted into a classifier, all of its corresponding training data is discarded. However, the discarded data actually are used to compute support and confidence of other rules and will affect other lower ranked rules since rules normally have common training data examples. The use of static support and confidence may result in very large less-accurate classifiers.Thus, a procedure that amends other rules’ support and confidence is important. This paper proposes a new procedure named Active Pruning Rules (APR) to overcome the above problem so then the classifiers’ performance - especially predictive accuracy and reducing rule redundancy - will be further improved. The experimental results obtained from a number of University of California Irvine (UCI) data sets and real adult autism classification data set showed that APR is highly competitive to other AC and rule-based classifiers and often produces smaller yet more predictive classifiers.

A new recommendation system on the basis of consumer initiative decision based on an associative classification approach

PurposeThe purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.Design/methodology/approachBy employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.FindingsThe results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.Originality/valueConventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

A semi-supervised associative classification method for POS tagging

We present here a data mining approach for part-of-speech (POS) tagging, an important Natural language processing (NLP) classification task. We propose a semi-supervised associative classification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources. Our method uses a combination of a small POS tagged corpus and untagged text data as training data to build the classifier model using association rules. Our tagger works well with very little training data also. The use of semi-supervised learning provides the advantage of not requiring a large high quality tagged corpus. These properties make it especially suitable for resource poor languages. Our experiments on various resource-rich, resource-moderate and resource-poor languages show good performance without using any language specific linguistic information. We note that inclusion of such features in our method may further improve the performance. Results also show that for smaller training data sizes our tagger performs better than state-of-the-art CRF tagger using same features as our tagger.

A Comprehensive Analysis on Associative Classification in Medical Datasets

Association rule mining along with classification technique is capable of finding informative patterns from large data sets. Output of this technique is of the form if-then which is easy to understand for the end users and also for prediction. Termed as Associative Classification, it is having wide application on medical domain in diagnosing diseases and to analyze medical datasets which are unstructured, heterogeneous, incomprehensible and voluminous. Analysing these data allows physicians to predict the diseases as well as to take vital decisions. This paper presents a detailed study on associative classification and the phases of associative classification procedure. Several associative classification methods viz., CPAR, MMAC, CAR, CBA, CMAR, etc. along with their merits and demerits are also presented in a lucid manner.

A lazy associative classifier for time series

Association rule mining that mainly focuses on symbolic items presented in transactions has attracted considerable interest since a rule provides a concise and intuitive description of knowledge. However, a time series is a sequence of data that is typically recorded in temporal order at fixed intervals of time. In order to mining rules in the context of time series data, a symbolic aggregate approximation (SAX) representation that could discretize the real-valued and high-dimensional time series data into segments and convert each segment to a symbol is applied in this paper. On this basis, a modified CBA algorithm is proposed to discover Class Sequential Rules (CSRs) and make the final prediction at first. Then we propose a new lazy associative classification method, in which the computation is performed on a demand driven basis. This is in contrast to rule-based classification methods like CBA which generate excessive number of rules, but is still unable to cover some test data with the discovered rules. Various experimental results show that our lazy associative classification for time series can be interpretable and competitive with the current state-of-the-art algorithm. In addition, four different methods that select the mined CSR(s) are proposed for carrying out associative classification.

Technique for Prevent Straight and IndirectBias in Information Mining

For coercing the accommodating understanding disguised in the huge accumulation of database the information mining technology is utilized. There are some negative methodologies happened about the data mining technology, among which the potential protection incursion and potential separation. The last comprises of unreasonably treating people on the premise of their having a place with an exact group. Data mining and automated data collection techniques like the arrangement secured the route for making the computerized judgment like granting or denial the loan, race, religion, and so on. On the off chance that the training information set are onesided in what respects oppressive traits like gender, race, religion, and so forth., discriminator decision may guarantee. As a result of this reason the data mining technology presented antidiscrimination strategies with including discrimination discovery and avoidance. The discrimination can immediate or circuitous. At the point when any decisions are made on the sensitive attributes around then direct discrimination are happened. While the indirect discrimination are happened when the decision are made on the premise of non-sensitive attributes which are unequivocally connected with the sensitive. Here in this paper we manage discrimination avoidance in data mining and proposed novel system for discrimination prevention action with the post transforming methodology. We proposed Classification based on predictive association rules (CPAR) algorithm, which is a sort of association classification methods. The calculation joins the preferences of both association classification methods and traditional rule based classification. The algorithm used to avoid discrimination anticipation in post handling. We compute the utility of the proposed approach and contrast and the existing methodologies. The exploratory appraisal demonstrated that the proposed strategy is successfully deleting the direct or indirect discrimination biases in the first data set for keeping up the nature of data.

Integrating Noun-Based Feature Ranking and Selection Methods with Arabic Text Associative Classification Approach

Feature ranking and selection (FR&S) is an important preprocessing phase for text classification, and it is in most cases produces small valuable sub-feature space among the whole feature space and reduces the classification errors. As the associative classification (AC) approach is an efficient method and its training and testing depend on the way that features ranked and selected, the examining of feature ranking methods is very significant. This paper presents an integration method of Arabic noun extraction with four FR&S methods: term frequency–inverse document frequency (TF-IDF), document frequency, odd ratio, and class discriminating measure (CDM). Association rule technology uses the result of the integrated feature selection to construct an Arabic text associative classifier. In this study, the majority voting and ordered decision list prediction methods are used by AC to assign test document to its category. A set of experiments are conducted on collection of Arabic text documents, and the experimental results show that our AC method works better with extracted nouns and feature selection method than with feature selection method individually. The AC based on CDM and TF-IDF methods outperforms the other methods in terms of AC accuracy. As the results indicate, the proposed method produces satisfactory classification accuracy and it has good selecting effect on the Arabic text associative classifier.

Evaluation of an associative classifier based on position-constrained frequent/closed subtree mining

Tree-structured data are popular in many domains making structural classification an important task. In this paper, an associative classification method is introduced based on a structure preserving flat representation of trees. A major difference to traditional tree mining techniques is that subtrees are constrained by the position in the original trees, leading to a drastic reduction in the number of rules generated, especially with data having great structural variation among tree instances. This characteristic would be desired in the current status of frequent pattern mining, where excessive patterns hinder the practical use of results. However the question remains whether this reduction comes at a high cost in accuracy and coverage rate reduction. We explore this aspect and compare the approach with a state-of-the-art structural classifier based on same subtree type, but not positional constrained in any way. We investigate the effect of using different types of frequent pattern (frequent or closed), or subtree types (induced, embedded or embedded-plus-disconnected subtrees) to the performance of the two classifiers. Different rule strength measures such as confidence, weighted confidence and likelihood are also examined in our study. The experiments on three real-world data sets reveal important similarities and differences between the methods.